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Effective Computing for Computational Biologists*

Effective Computing is about improving your skills and adopting Best Practices to help make you a better programmer, and a better scientist. This is a curated short list of resources to help you achieve these aims.

We welcome community contributions. If you have a suggestion or comment, please post an Issue.

General

Organizing, documenting and sharing (including version control)

Programming and software design

Plots and data visualization

  • D3 ("Data-Driven Documents").

  • It is important to choose colors wisely so that they are visually distinguishable by most people.

R

Python

  • Python tutorials available from the Institute For Quantitative Social Science at Harvard.

Julia

Containers and package managers

Linux, the UNIX shell, and bash scripts

macOS (previously Mac OS X)

Development tools and environments

Bioinformatics and computational biology resources

Best Practices

Advocacy

License

Copyright (c) Peter Carbonetto, 2017

CC BY 4.0

This work is licensed under a Creative Commons Attribution 4.0 International License.

You must give appropriate attribution: mention that your work is derived from work that is Copyright (c) Peter Carbonetto and, where practical, linking to this Github repository; provide a link to the license; and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

Credits

People who have contributed to these materials: John Blischak, Peter Carbonetto, Matthew Stephens.

*Title adapted from Effective Computation in Physics.

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